ArcadeData/arcadedb

 
 
 
 
 
 
 
 
 
 
 
 
 
Scheduled suite Runs Status
HA integration (Raft, embedded) daily
HA resilience (Docker, fault injection) daily
HA chaos (randomized faults, 60 min) weekly
Load tests daily
Bolt driver-version matrix daily
Native image build daily
Benchmarks weekly
 
 
 
 
 
 
 
ArcadeDB is a Multi-Model DBMS created by Luca Garulli, the same founder
of OrientDB,
after SAP's acquisition. Written from scratch with a brand-new engine made of Alien Technology, ArcadeDB is able to crunch millions
of records per second on common hardware with minimal resource usage. ArcadeDB reuses OrientDB's SQL engine (heavily modified) and
some utility classes. It's written in LLJ: Low Level Java - still Java21+ but only using low level APIs to leverage advanced
mechanical sympathy techniques and reduce Garbage Collector pressure. Highly optimized for extreme performance, it runs
from a Raspberry Pi to multiple servers on the cloud.
ArcadeDB is fully transactional DBMS with support for ACID transactions, structured and unstructured data, native graph engine (no
joins but links between records), full-text indexing, geospatial querying, and advanced security.
ArcadeDB supports the following models:
- Graph Database (compatible with Neo4j Cypher, Apache Tinkerpop Gremlin and OrientDB SQL)
- Document Database (compatible with the MongoDB driver + MongoDB queries and OrientDB
SQL)
- Key/Value (compatible with the Redis driver)
- Search Engine
- Time Series (with InfluxDB Line Protocol, Prometheus remote_write/read, and PromQL
support)
- Vector Embedding
- Geospatial
ArcadeDB understands multiple languages:
- SQL (from OrientDB SQL)
- Neo4j Cypher (Open Cypher)
- Apache Gremlin (Apache Tinkerpop v3.7.x)
- GraphQL Language
- MongoDB Query Language
ArcadeDB key capabilities:
- **70+ Built-in Graph Algorithms** — Pathfinding, centrality, community detection, link prediction, graph embeddings, and more —
all available out of the box
- **Parallel Query Execution** — SQL queries leverage multiple CPU cores for faster execution on large datasets
- **Materialized Views** — Pre-computed query results stored and automatically maintained
- **MCP Server** — Built-in Model Context Protocol server for AI assistant and LLM
integration
- **AI Assistant** — Integrated AI assistant in Studio (Beta) for query help and database management
- **Geospatial Indexing** — Native spatial queries and proximity searches with geo.* SQL functions
- **TimeSeries** — Columnar storage with Gorilla/Delta-of-Delta compression, InfluxDB/Prometheus ingestion, PromQL queries, Grafana
integration
- **Hash Indexes** — Extendible hashing for faster exact-match lookups alongside LSM-Tree indexes
ArcadeDB can be used as:
- Embedded from any language on top of the Java Virtual Machine
- Embedded from Python via bindings: arcadedb-embedded-python
- Remotely by using HTTP/JSON
- Remotely by using a Postgres driver (ArcadeDB implements Postgres Wire protocol)
- Remotely by using a Redis driver (only a subset of the operations are
implemented)
- Remotely by using a MongoDB driver (only a subset of the operations are
implemented)
- By AI assistants via the built-in MCP Server (Model Context Protocol)
For more information, see the documentation.
Use Cases
Explore real-world examples in the arcadedb-usecases repository — self-contained
projects with Docker Compose, SQL schemas, and runnable demos covering:
- **Recommendation Engine** — graph traversal + vector similarity + time-series
- **Knowledge Graphs** — co-authorship and citation networks with full-text search
- **Graph RAG** — retrieval-augmented generation with LangChain4j and Neo4j Bolt
- **Fraud Detection** — graph, vector, and time-series signals with Cypher
- **Real-time Analytics** — IoT and service monitoring with time-series
- **Social Network Analytics** — materialized view dashboards with polyglot queries
- **Supply Chain** — multi-tier visibility with PostgreSQL protocol and JavaScript